Project Description

The spraying of groves, orchards and row crops such as citrus, apples,
tomatoes, and corn with pesticides is a vital operation in the
agriculture industry. Pesticides are applied to crops anywhere from a
few times a year to a few times a week. Additional applications are
needed when an infestation is detected, and the pesticides must be
applied rapidly. Spraying from the air is a fast way to deploy the
compounds, but it is costly, wasteful, and detrimental to the
environment, since it blankets the entire field. Furthermore,
spraying groves from the air is ineffective due to poor penetration of
the canopy. Spraying from the ground is more efficient, since the
sprayers can target individual trees or crow rows, but it is much
slower. Spraying is most effective when performed at night, since
insects are most active at that time and there is no sunlight to
breakdown the pesticide compounds. Night spraying is also safer since
there are fewer workers in the field to be exposed to the
chemicals. The problem with night spraying is that poor visibility and
fatigue limit the operator's productivity and increases the chance of
accident and injury.

The objective of this project is to automate ground-based vehicles for
pesticide application, such that a single operator, from a remote
location, can oversee the operation of at least four spraying vehicles
running at night. The potential savings are immense. There are over
6B application-acres of vegetables and row crops in the U.S. per
year. If only 50 cents per acre were saved due to automatic spraying
of 3% of the crop (the likely percentage of innovators willing to try
new technology), the net savings to the industry would be $90M/year.
As the technology is widely accepted, the savings would be in the
billions.

The project sponsors are NASA, USDA Agricultural Research Service,
and Deere & Company. We also thank Dean Remick of Eden BioScience
Corporation for his promotion of the project, and U.S. Sugar
Corporation and A. Duda & Sons for access to their groves.

Automation will enable:

Each worker employed in spraying to be up to four times more
productive, by overseeing a fleet of spraying vehicles rather than
driving just one;

Each spraying rig to be at least 20% more productive, due to
constant operation at the highest, safe speed;

Spraying to be performed at night, when the compounds are most
effective and the proper time and care can be taken to ensure complete
coverage;

Compounds with higher efficacy to be used, since human exposure will
be minimized.

In addition to the cost savings, precision application of the
chemicals will minimize environmental damage. The first year of the
program is focussing on tree crops, such as citrus and apples.
The second year of the program will focus on vegetables and
other row crops.

Technical Objectives

The technical objectives are:

Produce a primary navigation system based on GPS and dead reckoning
sensors with an accuracy of +/- 6 inches or better.

Produce a second navigation system based on camera vision or ladar
to serve as a backup in the event of GPS unavailability or
inaccuracies. The secondary system should be accurate as the primary
for short distances.

Produce an obstacle detection system with zero false negatives.
This will be accomplished by taking advantage of domain specific
knowledge. False positives will be handled via supervisor
confirmation.

Produce an operator control system that enables a supervisor to
track the locations of the sprayers, confirm obstacles, and remotely
drive the machines such that the supervisor minimally delays the
overall operation.

Produce an integrated spraying system capable of operating evenly,
at the highest possible rate, spraying chemicals only as needed. We
expect to achieve a 20% performance increase per machine across a
shift, and a 20% reduction in the amount of chemical applied per rig.

Project Status

In the last week of April, 2000, we transported our Deere 6410 tractor
to an orange grove in Florida to collect data and test the autonomous
navigation system for trees. For one of the tests, the system was
taught to drive a 7 kilometer path through the grove. It was then
put in autonomous mode and drove the taught path at an average speed
of 4 mph, spraying water enroute. The pictures below show the
unmanned tractor (front and rear) driving down a row.

The pictures below show a sequence of tractor shots as it executed
a turn. The camera was mounted on a tower near the grove.